Modern systems may offer users a relatively large selection of options. For example, systems may offer a vast number of products for sale, movies to watch, songs to listen to, books to read, and the like. These and other systems may employ recommendation systems to provide option recommendations to the users of the system in an attempt to present users with options relevant to the user. Employing an accurate recommendation system may improve user satisfaction with a particular system.
Some recommendation systems may employ collaborative filtering, which may include analyzing relationships amongst users and inter-dependencies among products to identify new user-product associations. The collaborative filtering may include neighborhood methods and latent factor methods.
Neighborhood methods may include computing relationships between users and/or products to evaluate a user's preference for a product. Latent factor methods may attempt to model ratings by characterizing both users and products by a set of factors inferred from rating patterns.
The subject matter claimed herein is not limited to embodiments that solve any disadvantages or that operate only in environments such as those described above. Rather, this background is only provided to illustrate one example technology area where some embodiments described herein may be practiced.